{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the ECCOv4 native model grid parameters \n",
    "\n",
    "## Objectives\n",
    "\n",
    "Introduce two methods of loading the ECCOv4 native model grid parameters.\n",
    "\n",
    "## Introduction\n",
    "\n",
    "The ECCOv4 model grid parameters are provided as a single NetCDF file.  The file you have may look a little different than the one shown here because we have been working hard at improving how what exactly goes into our NetCDF files.  \n",
    "\n",
    "This tutorial document is current as of Sep 2019 with the ECCOv4 NetCDF grid files provided in the following directories:\n",
    "\n",
    "https://ecco.jpl.nasa.gov/drive/files/Version4/Release3_alt (po.daac drive, recommended)\n",
    "\n",
    "https://web.corral.tacc.utexas.edu/OceanProjects/ECCO/ECCOv4/Release3_alt/  (mirror at U. Texas, Austin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two methods to load the ECCOv4 model grid parameter NetCDF file \n",
    "\n",
    "Because the ECCOv4 model grid parameter data is provided in a single file you can use the ``open_dataset`` routine from ``xarray`` to open it. \n",
    "\n",
    "Alternatively, our subroutine ``load_ecco_grid_nc`` allows you to (optionally) specify a subset of vertical levels or a subset of tiles to load.\n",
    "\n",
    "We'll show both methods.  Let's start with ``open_dataset``.\n",
    "\n",
    "First set up your environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import the ecco_v4_py library into Python\n",
    "## =========================================\n",
    "\n",
    "## -- If ecco_v4_py is not installed in your local Python library, \n",
    "##    tell Python where to find it.  For example, if your ecco_v4_py\n",
    "##    files are in /Users/ifenty/ECCOv4-py/ecco_v4_py, then use:\n",
    "\n",
    "sys.path.append('/home/ifenty/ECCOv4-py')\n",
    "import ecco_v4_py as ecco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Set top-level file directory for the ECCO NetCDF files\n",
    "## =================================================================\n",
    "\n",
    "## -- If files are on a local machine, use something like \n",
    "# base_dir = '/Users/ifenty/'\n",
    "base_dir = '/home/ifenty/ECCOv4-release/'\n",
    "\n",
    "## define a high-level directory for ECCO fields\n",
    "ECCO_dir = base_dir + '/Release3_alt/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 1: Loading the model grid parameters using ``load_ecco_grid_nc``\n",
    "\n",
    "Method 2 is super simple, just use ``open_dataset``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (i: 90, i_g: 90, j: 90, j_g: 90, k: 50, k_l: 50, k_p1: 51, k_u: 50, tile: 13)\n",
       "Coordinates:\n",
       "  * i          (i) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * i_g        (i_g) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j_g        (j_g) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * k          (k) int64 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
       "  * k_u        (k_u) int64 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
       "  * k_l        (k_l) int64 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
       "  * k_p1       (k_p1) int64 0 1 2 3 4 5 6 7 8 9 ... 42 43 44 45 46 47 48 49 50\n",
       "  * tile       (tile) int64 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    XC         (tile, j, i) float32 ...\n",
       "    YC         (tile, j, i) float32 ...\n",
       "    XG         (tile, j_g, i_g) float32 ...\n",
       "    YG         (tile, j_g, i_g) float32 ...\n",
       "    CS         (tile, j, i) float32 ...\n",
       "    SN         (tile, j, i) float32 ...\n",
       "    Z          (k) float32 ...\n",
       "    Zp1        (k_p1) float32 ...\n",
       "    Zu         (k_u) float32 ...\n",
       "    Zl         (k_l) float32 ...\n",
       "    rA         (tile, j, i) float32 ...\n",
       "    dxG        (tile, j_g, i) float32 ...\n",
       "    dyG        (tile, j, i_g) float32 ...\n",
       "    Depth      (tile, j, i) float32 ...\n",
       "    rAz        (tile, j_g, i_g) float32 ...\n",
       "    dxC        (tile, j, i_g) float32 ...\n",
       "    dyC        (tile, j_g, i) float32 ...\n",
       "    rAw        (tile, j, i_g) float32 ...\n",
       "    rAs        (tile, j_g, i) float32 ...\n",
       "    drC        (k_p1) float32 ...\n",
       "    drF        (k) float32 ...\n",
       "    PHrefC     (k) float32 ...\n",
       "    PHrefF     (k_p1) float32 ...\n",
       "    hFacC      (k, tile, j, i) float32 ...\n",
       "    hFacW      (k, tile, j, i_g) float32 ...\n",
       "    hFacS      (k, tile, j_g, i) float32 ...\n",
       "    maskC      (k, tile, j, i) bool ...\n",
       "    maskW      (k, tile, j, i_g) bool ...\n",
       "    maskS      (k, tile, j_g, i) bool ...\n",
       "    maskCtrlW  (k, tile, j, i_g) bool ...\n",
       "    maskCtrlS  (k, tile, j_g, i) bool ...\n",
       "    maskCtrlC  (k, tile, j, i) bool ...\n",
       "Data variables:\n",
       "    *empty*\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    title:        netCDF wrapper of MITgcm MDS binary data\n",
       "    source:       MITgcm\n",
       "    history:      Created by calling `open_mdsdataset(extra_metadata=None, ll..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_dir = ECCO_dir + 'nctiles_grid/'\n",
    "\n",
    "## load the grid\n",
    "grid = xr.open_dataset(grid_dir + 'ECCOv4r3_grid.nc')\n",
    "grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot two of the model grid parameter fields ``hFacC`` (tracer cell thickness factor) and ``rA`` (grid cell surface area)\n",
    "\n",
    "First we plot ``hFac``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HpAck3STpucsWdPc7NS3l/vxs2QslvcrdH+unqeGV7mtJMrN/oOm8kl/upXX5qNLPP63pbrY/lPRXms5veLW7/1UP7cxBlb4+XdLvS3pY00mVl7n7rX00MpWIwcEi7l8BACC6F73oRf6xj32s0TrOOOOMibvvbalJpTDHAQCARCJ+eWdXBQAAKI2KAwAAiVBxAAAAWaPiMEBm1koE3d7ernW/yWRSa/3F+xX/Vmd9Ze8zs+PuJ1W5w1xbfd1E3depb/v379fOzo5tXvJIu3fv9q2trZZblK/JZFL7PT3Uvq74me6Nu9d6T7f02CErDgSHTLXxZiyc6W2pffuePPfN4rKTyeSJNmxaz7L1VbmfplcsDCfaBmPv3voTt7e2to54fbGamdV+Tw+5ryt8pkcj2nZAYlcFWrBqY8BGYrWIGwsAkKg4ZKs4aNcdpDbdb10waGNgrFqxiMLdZWaEB4xKbp/jtkTcDhAcRqCrQWrVOldtIOoGgflAm4N5H0TcWABN5PQ5HjuCAzaqWllY/N2y28vW2UaVZKhyez4A2hFx28Ach5HoMumbGd8k1oi4YQCAVag4jEjdXRbFCsG63Q2Ly7Qp6nwHQgOAdSJuIwgOI1M1PCwO1KtuF8NF8TGWDfTzY5frhIAoASLixgDoGvMcnorzOCCMro+GKGvZRqTs4w95AxRxQwAAZREc8BRVKhKbJkFWfYyqA+4QwwOhAUAVEbcZTI5EZVXLa+uWbVqqa/qhi3K6ZyAXEQdKPBUVBxyhzjyI4vLF28X/h7KLBACGIuK2j4oDKtt0+GWkD8JQL7wDAENFxQFPUafSUDzSocz9IwWLKnJ9XgC6E3G7QXDAExbfwE0nHi4GiYgfEADoUsTtIrsqsFSTcyysug0AiI/gMECpZvqP8dTRbfb12PoOQDPzXb1N/qVAcMBTtHWYJQAgPTM738zuNLO7zOyyJX//WjP7LTP7rJl93sxeuWmdzHEYqGWDctffaKuczpnQsFyX1+sAkJ8utxVmtkvSVZL+saSDkm4zs5vd/Y7CYm+X9FF3f7+ZnSXpFklb69ZLxQFPsSk0RD23et/GuNsHKCt1qX1EXiLpLne/290fk3SDpAsXlnFJJ8x+PlHSfZtWSsUhkNSnWOZDXl3dK5ICGIeOtw+nSDpQuH1Q0ksXlrlc0q1m9v2SniHpFZtWSsUBT7HqGhIMfgDQvhYmR+42s32Ff28urH7ZN83FjflrJV3r7qdKeqWkD5nZ2mxAxSGYri4rzfkWACCkHXffu+JvByWdVrh9qo7cFfFGSedLkrv/gZkdJ2m3pPtXPSAVh6DYRxgHcx2A5fhstFJxWOc2SWea2fPM7FhJF0m6eWGZP5P0ckkys6+XdJykL65bKcEhAwSIYVo8HfeQDb19AKpz98OSLpX0SUl/rOnRE7eb2bvN7FWzxf61pDeZ2eckfUTSG3zDgMKuiozUHaQIHd1KPam1DN4DQP/6+NLn7rdoeohl8XfvLPx8h6RzqqyTikOGGASq6aO/hhwehtouAMNExWHkCBngPYBUhhyo+xLx80dwyNTiB3LZboyIb9iuVN2ArVt+Vb/O70O/A0+KMg+oKxG3B+yqyNi6czJEfLN2reo5LOr0Id+wAERHxWEECAnlVf32UwwCZfuZ1wPAXMTtARWHzEV8Uw7Run6MdNglADRFxQFYou7uibJBjbkOAKSYX+6oOAAlRfyAA0DbqDgAJS1WCZpUDQghAKJOVCc4ACUwdwFAFyIGB3ZVABsUQ4OZVT6KAv0j6AHdoeIAJMQkyW7Qp4gi4nuVigMAACiNikMmKJ93Y1nJu80+5vUCpsZ6VtWI2wAqDoHN97cv7oNHNav6rOvQAAARUXEIal1AYL95cwQwAH2IuK0mOAS1eD4B1Lf4wSWUAehD1PM4sKsiI4SJfrBrKA5eH6B9VByCY1Jke6oMMgxIMfC5wNBFfI8SHIJbfNNFfBMC0RDYuzHWIyuiITgADTHvYTwWBzVeezQV8f3DHAcAKGGxyhB1YhvQFBUHoAV1vnnybTWOxddq2URkXkvUEfF9Q3AAEom4wRirVa/V4hE2vKbNjHF+Q8T3DMEBAGqab/THOOC1if6LheAwEnwb6h59PF7zowEWB8AxvB+Kz7nO8x1zaIg6T4bJkSPByaEwVn293yMOAEAdVBxGiA0cxqLvKlDxPARj+JxxeGpzEfuLisOIUXnoH33er2Ub5a5fg6jl56p4L48XFYcRG8PGbWjo8/R4DbpVpepA+Ij5fiQ4oJQhliDb2OhMJpNWN15D7Cegb5t21xAYnhRxe0FwwEZ9fshz2D+8rL8iPx+gTYSG+AgOGIwqoSHaxmeMh+n1jWrP8ET7nKYQ8T1LcMBafZwZL4cqg8TJgFIiNPSL9/i4ERywVteXuc1hA7Tq0ubrnhsDXbvoy/7k8JkdiqhH4BAcMtbW4LS4ji4GvXXrG/qGalV/bApdTc+4hylCGNAvgkNmlp2QZdGQNrKrzmgZrexfNzwUlxnS6xIJ/dafKJ/HSCK+fwkOGYl+at2ud4ukUqa/Im48hoDAhegivn8JDhlIMWO/qw125GtqtLHrgYGwPPqqX9E+j+gOwSGw1If4FTfcbW/Ex7qRYiAsb9XppAltiCTie47gEFDqeQuLh2h2uf4xYyBbbl2/NL2sc+owDkRAcBiwMgNH1xu2KoN4m0dxRA4Pbb0mDFrLtVHlKvv+4sgXdC3i+4qrYw5U6oHTzDprw6b1pn7uiKdqwK37GF1+LoAoqDgM1LoUWucbedlvZ002ipvWX+YslGyUj8Qui+Wa7pYAUuMEUGjN/IqNbb2hUm0sy1whL/JRFH2JuGFp25DDUy6nTF+Hz2Z3Ir5vCA4D1sYksDIf+LY2CqvWU/bsicAqm6pTOV4YbSjoNywiOAxc1W/ty+676jZiGfK37j7VmdjYpZxfE7YZ3Yv4/mFyZKb4wMdSdtIdr2s59BPQHSoOQVDuz0vZ3TrRrtmRGv2EaCJWHAgOw7Qj6Z7UjQhkT4P7tt7XbQ5ey9aVcHCs3c+TyWTHzDp7T/fdJz083mD7OjNNth2jRXAYIHc/KXUbxoK+7gf93B/6OhYqDgAAoJSo53FgciQAACiNigMAAIlQcQAAAFmj4gAAQCIRKw4EBwAAEokYHNhVAQAASqPiAABAIlQcAABA1qg4AACQACeAAgAA2aPiAABAIhErDgQHAAASiRgc2FUBAABKo+IAAEAiVBwAAEDWqDgAAJAIFQcAAJA1Kg4AACQQ9QRQBAcAABKJGBzYVQEAAEqj4gAAQCJUHAAAQNYGHxzM7G1m9oHZz1tm5mZGpaRl9DMA9G8+QbLuvxSSDwxm9lDh5tMlPSrp8dntt7j7e1p6nNdL+gFJZ0r6a0n/VdLb3P1wG+sfuh77+Q2SrpH0SOHX/8zdf7uN9QMA0koeHNz9+PnPZrZf0iXu/usdPNTTJb1V0mcknSTpZkk/IumnOniswemxnyXpD9z9mzpaNwBkIerhmBF2VVxuZtev+NuJZnaNmR0ys3vN7Aoz27VsWXd/v7t/yt0fc/d7JX1Y0jldtj2StvoZAFBexF0Vgw8OG1wn6bCkMySdLek8SZeUvO8/lHR7R+3KTdV+PtvMdszsT8zsHcyVAIB8hN2gm9nJki6Q9Cx3f0TSw2Z2paQ3S/qFDff9Lkl7VT5kjFaNfv4dSS+UdI+kF0i6UdPQ8ZP9tBgA4oi4qyJscJC0R9Ixkg6Z2fx3R0k6sO5OZvbPNZ3X8Ap33+m0hXmo1M/ufnfh5h+Z2bsl/agIDgCQhcjB4YCmRwbsLntkhJmdL+kXJf1Td/+jLhuXkcr9vMAl2calAGCEuq44zMa9/yRpl6QPuPsRBwSY2b+QdLmm2+vPufu3r1tn2DkO7n5I0q2S3mtmJ5jZUWZ2upmdu2x5M/tHmk6IfLW7/+8+2xpZjX6+YLZ7Q2b2dyW9Q9Kv9tdiAIAkzSaxX6Xp7uazJL3WzM5aWOZMSf9W0jnu/gJNjz5cK2xwmLlY0rGS7pD0gKSbJD13xbLvkHSipFvM7KHZv4/308zwqvTzyyV93swelnSLpF+R1Mo5IgAgNx0fVfESSXe5+93u/pikGyRduLDMmyRd5e4PzNpz/6aVDmpXhbtvLfnd5YWf96tQ9nb3ByV97+zfpnV/SxttzEHH/fwjmp4fAwCwRg+HVJ6ip85HOyjppQvLPF+SzOz3NN2dcbm7f2LdSgcVHAAAQCW7zWxf4fbV7n717Odl88sWk8rRmp5R+ZslnSrpU2b2Qnf/q1UPSHAAACCRFioOO+6+d8XfDko6rXD7VEn3LVnm0+7+FUl/amZ3ahokblv1gNHnOAAAgOVuk3SmmT3PzI6VdJGml1so+pikb5EkM9ut6a6Lu7UGFQcAABLpco6Dux82s0slfVLT+QsfdPfbZ+fX2efuN8/+dp6Z3aHphQ9/1N3/Yt16CQ4AAGTK3W/R9Ai34u/eWfjZJf3w7F8plYKDmdWORtvb23Xv2rrJZHLE76q0b37/4n2WrdPda534qEk/j9SOu5+UuhEAUBWnnF5hSB1TOG3yU8wH/rJtNTNNJpMnll+1XvTintQNAIA6hjQ+ltU4OER40lUG9VXLLnueEZ47AABtahQcxjRwmtkRz5cqAwCgrh5OANWJ2sEh4pNtiqAAABi7rI+qYKAHAAxZxC/htU4AFfGJAgCA5ioFh+3t7VqhgW/+AAAcqeOrY3ai810V89CwLDx0+aQJKwCAoYtYwe/8WhXrOqWrwZ3QAABAN7KeHAkAwJBRcQAAAFlLWnHoImmxmwIAEMHoTgBVRcSOAQAAR8pmjgOVBgBANBG/WGcRHAgNAICIIgaHQU6ObONqlgAAoH2DrDiUTWCEBgBAZFQcAABA1gZZcQAAYAyoOAAAgKyFrji4O/McAAAhcQKoDsxDQdmOJUiMD685gMgIDi0qOxgsdjoDyThE/LABQA4GGxyqqlqdAAAgtYhjVnaTI6k2AADQnWwqDhiPiAkdAJaJuD0bXMVh2S4HqggAAAzDoCoO84BgZk8cplJlkiQBAwAQScSKw6CCwzLzQDAPE8sQGMaF1xtADqKex2FwuyoWFQeJZQMGgwgAAP0ZZMVhXQIrVh4IDQCAyCJWHAYVHBY7cFkwiNjJAADkYlDBYROOtAAA5CTil+FBB4cyFQiMF7usAKB/gw4OcwwMAIAcUXFoGYEBAJCziMFh0IdjRuxQ9IP3BgCkMeiKg8QZIQEAeeIEUAAAIHuDrzhI1asOzLbPW8SEDgDLRNyeZV1xiPiCYD1eUwBIa3AVh2UXs9pUOVg8MVRxHcyRyMey0MBrCyCyiF+GBhUcipfV3mRTZ6+7mibiITQAyFHEcSq7XRXLTksddebq2M1ft3WvHa8rAPRrUBWHMlYNFKtCQvH3q9bFN9fhIRAAGIOI27pQFYc6HVwMEhFfoDGq8joR+gCgX+EqDquUHWyWTZZkAmW/ipUerngKYKyi7kYPVXFAXpp8YNbtggIAdKeTikMXRzRUXR8Dy3Btem04IgbAWETc1nUSHOp2xGLZet16VpW1y8xnYHfFMNQNCLxOAHJBcGhZlwPEuqMsio9PoOhG0wmQET9sAJCDQQaHoQzUDE79K3PWUF4XALmIuD0bXHAYSmhA/9aFhogfLgDIUZLgsGrfdhuhocxgw8TJ9KrMb+B1ApCriNu3JIdj9tVRy4JI8XdUN9Jq45okAIB+DW5XRds2DU6Eh+EiNADIWdQTQGUfHPrG9S8AAGVFDA5JdlWY2RG7DBhoUcT7AQCGKdkchyjfzOumwe3t7dqP2eS+kXC5bABjNx8P6/5LIem1KoYeGupo6zmlfmN0bdMRLwCAYUo2xyHKAFHn7JFtP7cy4SFKfxYVD8mc/5xrUAKAZSJu87g6Zkl9vriTyeSJeR9lA0GkN9+qa4UAAIaPoyoqSHXdilxOWLXY/ujPBwCairgdTHbmyEiGcpnnobSjqjIXFAOAsYm6e5aKQyCb3mBNKyJlLjBVdh1RQw4AYL0kwSHypapTtb3OdR2qtHNVVaDpOgAAq0XcbjI5sqSUV2rs8vG6KpXleiQIAIwduyoqKJbf+6o8dB0aUiE0AAAVB7So60kzXcJ1wT0AABG3SURBVKybMAAA+eu04rBqglzkAaaPSX9tnXJ6VVWkSvurTphkUiQAlBdxe9lpcIjYIWWsGxznv9+7d2+fTQqLoAFgzCJu/9hVUVPxFMldXFdiCFWZdW1o+lwXj/4YwvMFAGyW7OqYOWtjEGyrj4Y8TyL39wEArNP0ypiptqEcVdFA7t+Sy7wpi/Moyr6JUxydAgBoR6XgMJlMdszsnq4ak5PJZLKnwX077ec2Bupl62h6BdEG7ard1wCQUsTKa6Xg4O4nddUQPIl+BgAMFbsqAABIJPuKAwAAaE/E4MDhmAAAoDSCAwAAiXR9OKaZnW9md5rZXWZ22Zrlvs3M3Mw2nr2Q4AAAQIbMbJekqyRdIOksSa81s7OWLPdMST8g6TNl1ktwAAAggR5OAPUSSXe5+93u/pikGyRduGS5H5f0M5K+XKbdBAcAAPJ0iqQDhdsHZ797gpmdLek0d/+1sivlqAoAABJp4aiK3Wa2r3D7ane/evbzsrPqPfGAZnaUpCslvaHKAxIcAABIpIXgsOPuqyY0HpR0WuH2qZLuK9x+pqQXSvrt2Zl7v1rSzWb2KncvhpGnYFcFAAB5uk3SmWb2PDM7VtJFkm6e/9HdH3T33e6+5e5bkj4taW1okKg4AACQTMdXMD5sZpdK+qSkXZI+6O63m9m7Je1z95vXr2E5ggMAAJly91sk3bLwu3euWPaby6yT4AAAQCIRTzlNcAAAIIGyZ38cGiZHAgCA0qg4AACQCBUHAACQNSoOAAAkQsUBAABkjYoDAACJRKw4EBwAAEgkYnBgVwUAACiNigMAAAlwAqiOmNnbzOwDs5+3zMzNjMDTMvoZAFBG8oHBzB4q3Hy6pEclPT67/RZ3f09Lj/NCSe+VtC3pOe5ubaw3ir76eeExf1PSt0g6xt0Pt71+AIguYsUheXBw9+PnP5vZfkmXuPuvd/BQX5H0UUn/RdLHOlj/oPXYz/PHeJ0G8P4CALQrwq6Ky83s+hV/O9HMrjGzQ2Z2r5ldYWa7li3r7ne6+zWSbu+0wUG11c/z5SW9S9KPddVeAMjBfJ5D3X8pRP9GeJ2kP5d0hqRnSPo1SQck/ULKRmWoaj+/R9L7JX2hl9YBQFARd1UMvuKwipmdLOkCSW9194fd/X5JV0q6KG3L8lK1n81sr6RzJP3n/loJAOhL5IrDHknHSDpk9sQ8x6M0/SaM9pTuZzM7StM5JD/o7ocLywMAlohYcYgcHA5oemTAbmbsd6pKP58gaa+kG2ehYT4P4qCZvcbdP9VdMwEAfQi7q8LdD0m6VdJ7zewEMzvKzE43s3OXLW9Tx0k6dnb7ODN7Wo9NDqliPz8o6WskfcPs3ytnv9+W9JleGgwAQTSdGJmqWhE2OMxcrGkQuEPSA5JukvTcFcvukfSInjyq4hFJd3bdwEyU6mef+sL8n6Qvzv705+7+WG+tBQB0xiLuXwEAILpnP/vZft555zVax4033jhx970tNamUyHMcAAAILeKX9+i7KgAAQI+oOAAAkAgVBwAAkDUqDgAAJBKx4lApOOzevdu3trZae/DJZNLauobmOc95jnZ2dmqdOrHrft7e3m68rsV1TCaT0utdXLbKfVesb8fdT6q9AgBAaZWCw9bWlvbt29dqA3I8LbG7a+/e+kfHdNXPbSTb+etVt31ttWNhnfe0ukIA6EHKkzg1wa6KkSi+OZsM3u7eKOwtPm5xXRE/QADQRMTtXvLgsDiglV22zPIpRHgTNG1jMTxUWdey+0ToLwDAk5IHh1UYUIajjapAcR1d7K4AgIgibgsHFRyqdmCECkQOmu6eWLxvxA8KAGBqUMGhqaYDXBuPn6t531ItAID2RNyecgIoVFYMD5uCGlUgAMhLVhWHVCImxjoWKzqbKjx1QwNVDQBjwOGYA5Di223EF72JxV0Wy55/nddhvj4qFADGJOIYwq4KlLY4qDepKDS5PwAgnawqDn2LmBSbaOv5FisLhAcAYxZxHCE4oJGq8xHKnPwp4gcJAMaC4IDK2piLUCYcMEkSQO4ibuMIDkhi1dkoF3+uc2prAEB3CA5oXZOKBFUGAGMScXtHcKgp4ovdlSZVgeKJpJYd3rkshBAuAOQg6nkcsjocM+ILgKmyrx1HYwBAWlQcaiCgLLepElA1HGy65DrhAUB0EccTggNqWXatijKHWi5aFjZW3SYoAEB6lYLDZDLpqh2t6fq0xRHTYZf6PE00AQJAbiKOKZUrDhweh0VNJkWWVaxMcE0LAEgny10VDCxpECoBoJqI28ssgwPSKF45c/H3dSxWGQAgNxG3bbWCQ8Qn2hbOIbBeGyGhzLo2HXEBAOhGthUHdlcMWzEoEMYAjNFoTgAV8UlieOocugkASK9SxWF7e7urdrSur2oD35a7tex1pL8B5CLi9iyrU06nsPiis3ukPWX6kv4GgH5lOceh68GEKkM3CAEAxibiWJJlcOgaVYb2VelDghuAXETclhEcKlj2AhMamqnbf/Q7AKRBcCipbCqMmB4BAGlEHDOynBzZ9gsR8YWNgKoBAMRDxSGAHPfpExoAjF3UE0BlGxz6OHNk35eTjoqQAAD5yDY4SJx2GgAwbBG/GGYdHLpGMAEANBExOGQ5OTKViG8AAACqoOKwRpkgQFgAANQVcQyh4gAAAErLvuJQdx5CxBQIAIgl4lhDxQGdi/jBAAAsl33FoQ4GOgBA1zgB1IBV2V0R8UUEAMQUccxhVwV6UfXDMU/iET9UAJCzUVQcymCA6tamis+6/p//jZNtAchNxLFnNMFhcXdFxBcrJ/Q/AMQ0muAgMVil1EbfF9dB9QFADiKOS8xxAAAApY2q4oB8cIExADmIWHEgOAAAkEDUI8fYVYGwon7oACAyKg4AACQS8csPFQcAADJlZueb2Z1mdpeZXbbk7z9sZneY2efN7DfMbM+mdRIcAABIpHiW3Dr/1jGzXZKuknSBpLMkvdbMzlpY7LOS9rr735N0k6Sf2dRmggMAAHl6iaS73P1ud39M0g2SLiwu4O6/5e5/M7v5aUmnblopcxwAAEikhTkOu81sX+H21e5+9eznUyQdKPztoKSXrlnXGyV9fNMDEhwAAEikheCw4+57V/xt2clulj6gmX2HpL2Szt30gAQHAADydFDSaYXbp0q6b3EhM3uFpH8n6Vx3f3TTSgkOAAAk0MO5aG6TdKaZPU/SvZIukvTtxQXM7GxJvyDpfHe/v8xKmRwJAECG3P2wpEslfVLSH0v6qLvfbmbvNrNXzRb7WUnHS/plM/tDM7t503qpOAAAkEjXJ4By91sk3bLwu3cWfn5F1XVScQAAAKVRcciImYU8fSkAjFXEbTbBISMR34AAMGYRt9vsqgAAAKVRcQAAIBEqDgAAIGtUHAAASKCHE0B1gooDAAAojYoDAACJRKw4VA4OnCsAAIB2RBxPK++qiPgkAQBAO9hVAQBAIhG/jDM5EgAAlFap4jCZTHbM7J6uGpOZPXXvSD9XVruvASCliBWHSsHB3U/qqiF4Ev0MABgq5jgAAJBA1BNAERwAAEgkYnBgciQAACiNigMAAIlQcQAAAFmj4gAAQCJUHAAAQNaoOAAAkEjEigPBAQCABKKex4FdFQAAoDQqDgAAJELFAQAAZI2KAwAAiVBxAAAAWaPiAABAIhErDgQHAAASiRgc2FUBAABKo+IAAEACnAAKAABkj4oDAACJUHEAAABZo+IAAEAiESsOBAcAABKJGBwGv6vCzN5mZh+Y/bxlZm5mBJ6W0c8AgDKSDwxm9lDh5tMlPSrp8dntt7j7e1p8rB+S9G8kfZWk/ybpe9390bbWP2R99bOZXSTp30v66tljfFzS97v7X7exfgDICRWHGtz9+Pk/SX8m6VsLv/twW49jZv9E0mWSXi5pS9LXaTrAjUJf/Szp9ySd4+4natrHR0u6osX1AwASSh4cNjGzy83s+hV/O9HMrjGzQ2Z2r5ldYWa7Vqzq9ZKucffb3f0BST8u6Q0dNTuctvrZ3Q+4+07hV49LOqOLNgNAZPMTQDX5l8Lgg8MG10k6rOnAdLak8yRdsmLZF0j6XOH25ySdbGbP6bSFeajSzzKzbzKzByV9SdKrJf1cH40EgGgIDj0ys5MlXSDpre7+sLvfL+lKSRetuMvxkh4s3J7//MzuWhlfjX6Wu//ubFfFqZJ+VtL+PtoKAOhe8smRDeyRdIykQ2Y2/91Rkg6sWP4hSScUbs9//lInrctH1X5+grvfa2afkHSDpG/srIUAEFTEyZGRg8MBTWft73b3wyWWv13SiyV9dHb7xZL+3N3/oqP25aJqPy86WtLp7TYJAJBK2F0V7n5I0q2S3mtmJ5jZUWZ2upmdu+IuvyTpjWZ2lpn9HUlvl3RtT80Nq2o/m9nrzOxrbWqPpJ+Q9Bt9thkAomCOQ/8ulnSspDskPSDpJknPXbagu39C0s9I+i1J98z+vaufZoZXup8lnSXp9zXdNfR7ku6U9KYe2ggA6IFF3L8CAEB0xxxzjD/rWc9qtI6dnZ2Ju+9tqUmlRJ7jAABAWCl3NzQRfVcFAADoERUHAAASoeIAAACyRsUBAIBEIlYcKgWH3bt3+9bWVkdNycv+/fu1s7Njm5c8Ev1czWQy2XH3k1K3AwDGoFJw2Nra0r59+7pqS1b27q1/dAz9XI2Z3ZO6DQBQR/YVBwAA0J6IwYHJkQAAoDQqDgAAJMAJoAAAQPaoOAAAkAgVBwAAkDUqDgAAJBKx4kBwAAAgkYjBgV0VAACgNCoOAAAkQsUBAABkjYoDSjGzkMkYAIaKE0Ahe2a1LvYJAMgIFYcBmkwmTwzSQ0ujy8LDqjYO9TkAwFBE3D4SHDrQ5jfzvnYRrHucTc9nWUAo3qfsc1hcbt3jRvywAcCiiNsygsPILQ7wba1r1d82BYMqIQMA0D+CQ0u6HMja/uadctAt89iEAgBjQcVhhFIPcouPH/FNCACIg+DQQOrQUFW09gJA7iJ+2SM4rLHqqIAhD8DFtkV8QwIAho3gsMSQg0EVxYmGuTwnAMhF1BNADS44DHGAG2KbyorcdgDIXcTgMKgzRzLIAQAwbIOpOBAaAABjQ8WhJkIDAADtM7PzzexOM7vLzC5b8venmdmNs79/xsy2Nq0zeXAgNAAAxmo+QbLuv3XMbJekqyRdIOksSa81s7MWFnujpAfc/QxJV0r66U1tThocCA0AAHTmJZLucve73f0xSTdIunBhmQslXTf7+SZJL7cNg3OvcxwICgAAPKnjOQ6nSDpQuH1Q0ktXLePuh83sQUnPkbSzaqW9BQdCAwAAT/FJSbsbruM4M9tXuH21u189+3nZwLuYVMos8xS9BAdCAwAAT+Xu53f8EAclnVa4faqk+1Ysc9DMjpZ0oqS/XLfS5JMjAQBAJ26TdKaZPc/MjpV0kaSbF5a5WdLrZz9/m6Tf9A37TzqrOFBlAAAgndmchUs13SWyS9IH3f12M3u3pH3ufrOkayR9yMzu0rTScNGm9Q7mBFAAAKBd7n6LpFsWfvfOws9flvSaKutsPThQaQAAIF/McQAAAKW1GhyoNgAAkLdWdlUQGAAAGIdGwYHAAADAuNTeVUFoAABgfKzKebLNLN6FwxNy91rpin6ubOLue1M3AgDGgKMqAABAaQQHAABQGsEBAACURnAAAAClERwAAEBpBAcAAFAawQEAAJRGcAAAAKURHAAAQGkEBwAAUBrBAQAAlEZwAAAApREcAABAaQQHAABQGsEBAACURnAAAAClERwAAEBpBAcAAFAawQEAAJRGcAAAAKURHAAAQGkEBwAAUBrBAQAAlEZwAAAApREcAABAaQQHAABQGsEBAACURnAAAAClERwAAEBpBAcAAFAawQEAAJRGcAAAAKURHAAAQGlHV1x+R9I9XTQkQ3sa3Jd+rqZJXwMAKjB3T90GAAAQBLsqAABAaQQHAABQGsEBAACURnAAAAClERwAAEBpBAcAAFAawQEAAJRGcAAAAKURHAAAQGkEBwAAUBrBAQAAlEZwAAAApREcAABAaQQHAABQGsEBAACURnAAAACl/X9WW4Kf7aBO7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x745.2 with 26 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ecco.plot_tiles(grid.hFacC.sel(k=0), show_colorbar=True, cmap='gray');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Model grid cell surface area [m^2]'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kPI4pp6eIayHMW9w3qmhURbIpIjnUMq+pgv4NmK2yzECVD0WdYGBcH7qxNlWQccC8JZsq0vMx5DU9KPWcjAMwUGQiujDGYAdoTzJwSMvKNEhkHIAz+CdvYqx9HAgcMG+uvU6R+1LrTi9+7lN2x8h4WVnnSAApdSZtypsIqkrHSbSBwAEomwUyLzDI6+8gkXUAejf8Zoqx9nEYY5mB9sUZhHgI5r7Uuvhn2ZwNWUEFgAaGHwDUNdamCjpHArGszpFlnSTLOk4CwMSQcQCSkpNBFfVfSGYWstaTcQBasq6pZh3G2lRBxgEz54q+lBI3u6mSdSDjAAzUGBsBxmGMwQ7Qoh1Jjyj6ktmv6CNh0Yn/hM6ddjrOQiQzElkPMg5AgaKew3nr4uXpDET8eny9kcfax4HAATO3K2lTe184ieBBys4u5I2+SL/m04XJG9/JGs3x1YaZi5sq4isW6ZzMQ9Yts4v6OpBxAAZi2P0jyDgAoxRnHKToI5zMPKznBwhZyDJg9sqaDapkKOqc9MeXARnjVwadIzFzu5KOKwoeNrWXfVh0mCzqKJmU1VQBTNYQrpOTp9whlGc+xhjsAC3akvSA9j4K6SucjeyAoSgTkTVME5issqv8ovV5HR3ztgvJKKS3iYOK4TVZjLWpgowDZi4eVRF3kNxW9AWT8+VU1m+BIZmYjTZPeXWvYfP2y1o+xlP0MJFxwMztKAoa1hQFDPt1zsQMBABAgDgrUJY1CNFVX4VhZR/GOgGUuXv5VsBEmdl9km7vuxwjcYW7X1JnR+q5Mup6OWrXcxuebubXNjzG86Qb3f2qVgoUaIzBDtCaPr805oR6Xh7qGl0jcAAAoAdjbaqgcyQAAAg2xmAHAIDRYzgmAACYPDIOAAD0YKx9HMZYZgAARo+mCgAAMHlkHAAA6MFYmyrIOAAAgGBjDHYAABg9+jgAAIDJI+MAAEAPxppxIHAAAKAnYzwJ01QBAACCjTHYAQBg9EzSetOz8HYbJamGjAMAAAhGxgEAgB6YSWtkHAAAwJSRcQAAoAdm0vpq36WojsABAIAetNJU0QOaKgAAQLARxjoAAIxfK8Mxe0DGAQAABBthrAMAwASYpBF2jiTjAEmSmb3JzN63eH6lmbmZEVh2gLpeDup5eajreeEPOxNmdiLxckPSo5J2Fq9f5+7vaPG9/qGkX5T0/MX7vN/d/11bxx+6ZdW1mf2ypO9LLFqX9Ji7H2zj+EO3xHo2SW+T9AOSDkj6nKR/6e43tXH8MVhiXZ8v6Wcl/QtJ+yV9WNKPuftWG8cfHNMoz8IjLDLqcPcD8XMzu03Sa939j9p+HzM7T9IfSnq3og//jqSvbPt9hmxZde3uPyLpRxLv9QFJu22/z1Atq54lfbekH5T0PEm3S3q7pF+T9OwO3muQlljXb5R0laRnKEri/z+SflLSWzp4r/6NNHCgqQKSJDO72sw+lLPuQjO71syOmtldZvZ2M8trmXu1pLvd/X9395PuftrdP99ZwUeoxbpO7neBpJdK+mDb5R2rFuv5yZI+4+5fcvcdSR+S9DVdlXuMWqzr75T0i+7+gLvfpyhz+YNdlRv1EDggxAcVzYj+VEnPkvRCSa/N2fabJN1mZh83s2Nm9mkze+aSyjkFVeo66aWS7pP0J90VbVKq1PNHJD3VzL7SzNYlvUrSHyyllNNQpa5t8Ui+vtzMLuy0hH1aa/joAYEDCpnZEUkvlvSGRQbhXknvkvTynF0uX6z7RUlPlPT7kn530YSBAjXqOulVkn7V3b3LMk5BjXo+KulPJd0iaVNR08WPL6OsY1ejrj8u6cfM7BIz+wpJ/3qxfKP70iLUCFtXsGRXKOp0dzTqIyYpCjjvyNl+U1Fa9+OSZGb/UVEb5VdL+qtuizp6VetakmRmT1LUEfWHOi3ddFSt57dI+keSniTp7xV1SP2UmX2tu5/quKxjV7Wuf0bS4yT9N0UdMH9FUZbi3m6L2ZORDsckcECZOxR9gA+7e8gNXD8v6bndFmmyqtZ17JWS/tzdv9RNsSanaj1/naTfcPc7F68/YGb/h6J+Djd0VMapqFTX7r4p6fWLh8zshyXduOhbgoGgqQKF3P2opE9KeqeZHTKzFTN7ipk9P2eXD0n6JjP71kUHqDdIOibpr5dU5NGqUdexV0r6QOcFnIga9Xy9pO82syOLbb9f0VX0rcsq81hVrWszu8zMnmiRb5L0Zk11RIW0N6qCPg6YoFdKOk/SzZIelPRRSU/I2tDdb1GUyv3lxbYvkfRd7v7Ycoo6esF1LUlm9t8p6lfyW0sp3XRUqef/oKiZ7b9JekhR/4aXuvtDSyjnFFSp66dI+nNJJxV1qnyju39yGYXsxUgDB6MvFQAAy3fVhvkNT212DPuCbnT3q9opURj6OAAA0JcRdo6kqQIAAAQj4wAAQB+YchoAAEzdCGOd6TM74NKlihq/1hXFd7b4uaLMRrF4dfw8udxyli+DZzz3nOW7qeVn7CxWxhvsStpaLN+R9OVj7n5JneJFdX2JospbV1S3KzqnruOXcT2v6ew/SfLKIV6mxe77UodckWxtWyurLtOuVhaPNe2ceR4/VrWjNW0nlu1oVbta05ZWk8t2XLYr6bFF9cRVtp2oquTyeLtdaXdX2tk9Z/GZx/ai1h+SdMq91n/OhpkfXtTwRfsU3WPyoKRD0qm1fXpQF2lL69rS+uK3it5mTTuLpVvap9Par01tPHBaOi7phPToaemUolnHzvxNE1W9lvpLrkpaX5FW0n/i5EdtNfHzvNTrVclXpK3VtUXN7/2l9sq+9zh3WfQX39aaXCva3TH57qq0a1FFx5V+6431/6cPHXZdcuXZf+/kR2dHe5/B5PJdnf3ZjJcpsfzM+p3ECqU2UsnP9HbJN0wfM2951ntl7VvW+f8huZ9c1rfhuZaQcTCz90v6Dkn3uvszMtabpF+Q9O2KPk6vdvfPFh2TwGGQLpX085IOSTqi6O6yyUdi9tWyYTrpITtrqZ/p5U2lp3jZzli+nVqe9zgjPjUkH/dIemTx/Ptvr1/gSxTdxXe/9ur6oKI6XvxcU3TyTz4OZyx7nM7edk3RCfLpi9cH4p+u8w8/qI0Dmzpv9VFtaFMbOqWLdb82dEobOhWdIHVKB3VcF+t+HdTxxfJo2RHdq4M6fub1wYdPa/2koul2Tkp6OPHz7tTrk4tlJ6PHqZPS8ZNRbd61qNG4ZjclPaAo9rimfiXrcZJ+QtJlkl56paL7TH6LpBdIn730yfqoXqa79QTdqyM6roN6TNEM5Rfrfl2qe/REHdVX6RY9U1/Qsz/819KnJH1G+uLfRDOOfSHxXuva+6Q8XtGnKP6rHpJ02X5p4wJJyceFiiZIj58nlyVfH5K2LpDuvvAibWpDx3VQp7Sh4zqge3Vk8fzgmb/gPTqiTe1f/FU3zjw/pov12M75OnViv06f2JBOny8dM+mEpNOSvsPq/09feqX0MzdEx3locbzk4yFFn6/4dfz8hM7+/CXXp5frkcQbxne83k48Ty/bTi3fzNgnfp58He+T3j+5X3rbtKJ5p36pYN2SdN858gOS/k9Jv5qz/sWSnrZ4PEfSexY/c9FUMUir2vu6i8/864tHgbKTf17Q0KXQ9ywtU/z7x/WxX1EdXdykdMqv65wClY2dzgrSgMmp+s9dtH36e63ke65wu9B958Pd/0RR/J/nJVrc58bd/1LS48wsd+4Yia+2gVpXdPW7puikFl9DxSfMAkV/0e3F+u2MbbfV/L8hL9uQtS5prWS99iu6Qol/9/jLIX3VUce6ouvSuK7T2R1lT7ZSNAkLwQNmZV17n8P4eXJZ1rZ565PSX1bbGfvFr5PbprdJf9ENyDA6R16ms+8dcudi2dG8HfovMjKsaC9QSF5lp6Lp0NnDkkFB3vP4dfr4ZccNWRcaQKTf86xt11Mb7Fc70c6KoiR2fMz1xMPCZ2vrqukHGKzSiL/GMUICihB5x2mjzINz2MyS90y5xt2rtCxm9fEo7BzC19sgmfZOYukAIqcfT92/ZNG5t+7nq+5+uZ/pdFiebM9sI3CIMxnpOle16V35NGG2klnArHVVg4GyfbIyDKFBSPLKqWftZByONZw58k5Fd36NXa6oF1Qu+jgMUnwyKwga2pyn/JzOiD0fR8r4/eJPWFwXGR1Fa7HFMTd0dtBge+XIKldeQFHUMRWYjHQGMGtdur9BlX4OyX3S71XUjyH9HmXb9jegYkCuk/TKxI3FHl7cnCwXX2uDlL4KlhpnGtJNFFn7ttlUkbU+NKgozTysKerz0EbaMa7rjKAhLgtNFUAHsvoiVPk8F/VrGInlDMf8sKRvVtSkcaeiu42uS5K7/7KkjykainmroiFsP1B2TL7eBmlVhVfS+xY/11I/sxQFDGWBQtVzct72VQKI+Lsg+budTm4Qn9Tj+ml6k7Z4VEVGwJAcVpl8vpZ6nhdYkHHApGU1EaQ7MWZ1akw3a+Q1MWR1ukxvmxVspL/gklmHAQYXHQ/HdPdXlKx3Sf+yyjH5WhuTdEo862eIvH4NXTT51bmAyPqZe6ymqca4qSJVjpBgIGudMp4DWGirc2JWhiEkgxF/1gcYQIwIX29jkBckFKXHi0ZPxMuyjtGWkOxD0YVDOmgIOXYbQjMKWX0bpHMDCT5hmKSszILUvBNkSGARknUoWzeQpo1hDMesjM6RQ1V0xZv8mX6ep+hE3nbnyNAmiyxlWZWyvgZNVGl6KFqWfg7MSp0OP+ltkh0s11LL0tuULcuzGHKNyvh6G6qik1DICSrvSr2sP0RXso6ddzGQ1WTadbnzsgzKeZ38mV5GxgGzkNV/ISlvsqYuy5L1Pm01j3SAjANakxw8UHTyqnOF28fnp8poiuTzst+9rRNzSJNElSyEcpYBk1dl+ui8LEOT98tbzgexTdTmEMW39Sv769T96w00+JZ09u+UlWnIWn9a9Zny+zOkR1VkrQ8NMIBJMUUjmpJZByl/pEQ6jSidO2IivW1yeXpZVoYh/Z4jmHratIybXLWOr7Wh4oq1vHmijQxkOrtTllEoa7bIy0AAk5MOHmJFTRJlzRUhzRlF24SWZSDNFzRVoDXJk1kdY/pHbPI7tnFFn/zgNm2OoKkCyFCluSK9PHlX4KL043rO8iJronNkPXytDVXIX6brIZXLki7/Mn+v+Hujal8GMg6A9rIOsXSTRGhnyHV1lwEY+NwNI/yeGGGRcUaVv94AsnKSwsq87P/KosxFeh0ZByBDXpCQ1U+h6BhVv6jy9slaPpC5GyaAr7UhKmv3qvLZGkrAEKuTTejyvzTZLFTW2TG0M2RWxgKYrDjrkDVFtDJex9mFsk6NRR0gizpOKrFcGceWBhNA0DkSrcm7i3Qs6/MwRWXNn22+T9Exs7IORU0VyngOzEZI5qBJ58SifckqLANfbWOQF0SUffaaBBZ15l6oevyyk3Ub71NFSMYgqxxZ2yQDiakHeMCZzkJFmQWpWXNFURYjKztRpM6cER0Y6aiKERZ5BpId9rKyDVWCh1B1j1H1VtwhQk/WZcFHiNAPblGWIbmejANQQdmsk/GHqEkWoehLsudRFSMNHBiOOVRVOtc1uepv6z4VbR2zyu/b5gcur7kiLxgoKgPBA2aprHdwF1f5zBzZB2pzyLKuqvMmPqqSeVhW6rxqR8iik3BWFqKN3yM9Z0ZWM0WV13nbALMR2s8g+SEuyyzEx8zrUJk+XlY5Bto5bITfEWQchigkfTXCf7bKQuqgi6xDlT4OIVkKYPaqTNAUrw/NUBRtl7WOD2dT1ODQtd2noeq+Rbe5r3KMJv9pXXaUrNLEmVeOoqwInzBMUe73UDqLEJJ5qDoSosqU0wM30uGYZBzGIO/k09VJKaSfQhf9I5KW+TtXzWxUaVIBZiMvCs/q59BGf4cm/Sf4oDZB7Y3FMgLppiMruv5v6vL4oYFK1c6RwFQVfid1MZ9Cnb4TRfsN4IM60lEVIyzyTHTRj6fPDF4bQyeTuso8FPVvqHssYGqCL2TKTvZ5J/l4efp23XULONCJoUYaONBUMXRlI5za+Kdrq8mhreMs43euqqijZHokBTBbRWfCqp0YQ7Yr+sCl12W9B3fHrIOvOaDqCJay5gk+VZiLQfVFDLVWU6MAACAASURBVJ0xcmCZBzpHYnS6+NAP5oukgqqdMcs6Q9JUgSkL+t/OG4KZ10Gybvagyn5oA7U6F0M4mbfdz6EPYy8/MHhFkzyF7tt0myWhjwM6M/c0+LJ+/y7niwBmpcpQyaoftIHcoGrG+Gqcsy6zEGPNLhQ1MdRttgCmJv2/ftZ3iUnyxIZFoyaqvmneSInkuqypptPvNZCsw0gzDiMsMgAAEzDSwIGmCgDAEoScIbO2qdM0EbIPTR51jTDWAQAMSu1hmXlNBqFNGnVnkxwQhmMCAGajcFjmGK5LmQCqjjH8ZQEAQ1I6JX6yg6TUXmfEosxB2TTTA8w60McBo9PHTaOGruiun3nfOdsl6wEkZPUtaDIkk74KyzbWr/d52c55PhfJoZ1dDvNMH3uOdQ20Lu9Kv8qNqOpmLAaedRhpxmGERUYtQ/i8TOG/bazzUwCzMIQvuoroHInR6er21GNT1gyRt7xofVt3CwWGJvgW9HWaEeKD0QQxVGP8igfaVZZFCGnCSDcn8ckCMgwhIzCEMiyMtKmCjMPQZfVvaLvPQ1t3cWzrOMv4navKyh6ky0aGAQjUpHNjVkaC7MQyjTDWmYkuTkB9Btpt/6d1MZKh7WMRSGDW+pojYUAZhTJkHNCpZXwO6mYM2so0lOn6plwhy7OyDKHHAmaNrMBUjDDWmaGqHfeaChmS2PV/Tl4/gS76D4T0cVBim6Jggf4NQEfaGpI5IKZRjqrgK27o8q5466qaxWur70MTWZ0T2/rP9fJNSsuR/plcT/YBU1Q6cySC0FSB1rjKP5Bz+MCG1EFX/RLy+iiEBHIEDJiLZDPlCE+AqIc/9ZDltaM3bVdf1tVC1f+urExC8mo+edy2yp4M0vKOm/Xe6X2Sj/QyYOpKP5NNZo+s84Ev22dAHShHeBYm4zBUVUYN1P3/L528pYdjVvl9u8g2lC2v2jlyIN9NwDTR4bIPI4x1ZiBudw/JLLR1Ysq60q+zXxtC+jS0FTzEGYey3yMrK5HcL51xaKNswOy11bFxoB0kR9rHYYRFnqG6oyqaZOO6/s8IOVHnje7o8iZXIeXIa05Jvs5qYgEQiA/PkBE4DFFZ58i5fJ7SwyDTy9PP236fvPXpwKAoCwHM3kCv9oeA4ZhoTcioilBDO4nV+Y/rMohK1vVpFd+857SkfYn3zevvkF7HpwwYoAF8MGmqwNKVXSknjekvXeX36vr9srIN6edZ8zjkze0ATAEtCbPGV9pQVflQprcd21+1bh+O0G2KJDuiFvVxyBp+mddUQf8GAKHG9n0thmMOU5w+r3viGdMJq8nv2MY8CcmmivR8DHlND0o9D1kHzFLWWXGEZ0qchb/gUIVctU79r1d20m3jpFzUnyQr0xC/LxkHzB3/483RORKt2dW5V9Ppv1TWbIqhlv1Xb9LsUvV1Va69TpH7UutOL37uU3bHyHhZWedIAEuQHr1BZNMVAochSl8FF025nNVpr0gff/HQz2/eMMuiYKHN4Zh5fRzyAoO8/g5KLQOALIyqQKvK0udlAUPW8rK/dtP/hrJmldBsQVlfgbYDh/g4yeGY+1Lr4p9lczZkBRUAkIXAAa3KOuGUDf0rkre+7ftUxIrKXySk82FXJ+Oi9y4aTVH3bwIAI8RX2xiUBRHJ7dYS65XxvGhZm/ImnirqcJjUV3+B+D3K+i8kA4as9WQcAISgcyQ6lT7h5mUgynSVfaibZUjvXynD4EUrA7iiDlVrivKGyg9a0sFAOoAg4wBgBvhqG6QdSacWz/cvftre6rgtXioPGooyD037PGRlE7LWp6/Ki4KJrJPyWeJAYTOnEFXtSHpEUY/s/ToTQGxLOqFzp52OsxDJjETWg4wDsGTp+2GM4MNHHwe0Z1fRiXE98TNxRZxUZzRFWxmHvEAhvT4kYCg6zhnxcJOt1M8m4rqOj5MIHpJlSQcGZWUm44CpG8F5Gd3gq22Qkiez/YnlGSe0Nv6Cbf0X5PVrqOOcE3Q6aNhcPG961724qSJurpDOyTyk+5HkZRiUek3GAbMXEmXPGBkHtMe1d2KUzk0XNMg8pBXtU7WpIrlfne+GoExDHFCdSixrIg7SpCizk8w8rOcHCFnIMgCogsAB7YlPZmuKTmZSZooheaINDQCa9HkoOm5WebKaKMqCitzmgK3EimQA0UbgcFxn1/Xa3mPbsjMMWeVOv+bThakiaTBrfLUN0pake7TXTJH8uSlpI3/XkI6SXQ3PTAcFobMoln4JxUHCpqJMw6akB3R234S6thbHWku8TtrIDhiKMhHppgsAAzSQD+cIh2Nyd8xBinv6xyfG+Mq65Oq67HMQNLyxZaHvWVqm+PeP62NTUR3d36R0yq/rnAKV9VtIBxfA7DXNCmJoCBwGaUdnp+LTJ83E3AXpq+G8znsh67t8VC3bGcl7jKfrYlNRM0MTcV0nA7NUtEMAACzZTD50cR+HJo+ytzB7kZndYma3mtkbM9b/AzP7YzP7nJl93sy+vfSY7k0n0EHbzOw+Sbf3XY4RucLdL6mzI3VdCfW8PNT1ctSu5zZc9QzzGz7a7Bj21brR3a/KXGe2KulvJX2bpDslXS/pFe5+c2KbayR9zt3fY2ZfI+lj7n5l0XvSx2GA+vxHnhvqejmo5+Whrkek+1EV3yjpVnf/kiSZ2UckvUTSzYltXNKhxfMLJd1ddlACBwAA+tJt58jLJN2ReH2npOektrla0ifN7F9JukDSt5YdlD4OAACM12EzuyHx+OHEuoxJf865wc8rJH3A3S+X9O2Sfs3MCmMDMg4AAPShnaaKY3l9HBRlGJ6UeH25zm2KeI2kF0mSu/+Fme2TdFjSvXlvSMYBAIBpul7S08zsyWZ2nqSXS7outc2XJb1AkszsqxXdvu++ooOScQAAoA8dd450920ze72kTyjqTfF+d7/JzN4q6QZ3v07S/yzpV8zsxxU1Y7zaS4ZbMhwTAIAeXPX15jf8YbNj2KX5wzG7QlMFAAAIRlMFAAA9ce5VAQAApoyMAwAAPXCTdkZ4FibjAAAAgo0w1gEAYAJGmnEYYZEBABg/N2l7tWnif7eVslRBUwUAAAhGxgEAgB64mXbWmp6GH2ulLFWQcQAAAMHIOAAA0JOd1fHNAEXGAQAABCPjAABAD1ymHY0v40DgAABAD1ym7REGDjRVAACAYGQcAADoyc4IT8NkHAAAQLDBBw5m9iYze9/i+ZVm5mY2vhBt4KhnAFiuuHNkk0cfej8xmNmJxMsNSY9K2lm8fp27v6Ol93mVpH8t6WmSHpH0nyW9yd232zj+0C2xnl8t6VpJm4nF3+Hun27j+ACAfvUeOLj7gfi5md0m6bXu/kcdvNWGpDdI+q+SLpF0naSfkPSzHbzX4CyxniXpL9z9eR0dGwAmYazDMcfQVHG1mX0oZ92FZnatmR01s7vM7O1mlvlXcPf3uPufuvtj7n6XpF+X9Nwuyz4mbdUzACDcGJsqBh84lPigpG1JT5X0LEkvlPTawH3/saSbOirX1FSt52eZ2TEz+1szezN9JQBgOkb7hW5mRyS9WNLj3H1T0kkze5ekH5b03pJ9f0DSVQoPMmarRj3/iaRnSLpd0tdK+g1FQce/X06JAWAcxjoB1GgDB0lXSFqXdNTM4mUrku4o2snM/pmifg3f6u7HOi3hNFSqZ3f/UuLlF8zsrZL+rQgcAGASxhw43KFoZMDh0JERZvYiSb8i6X9w9y90WbgJqVzPKS7JSrcCgJmJOkeO7zQ82j4O7n5U0iclvdPMDpnZipk9xcyen7W9mX2Log6RL3X3/3eZZR2zGvX84kXzhszs6ZLeLOl3l1diAECXRhs4LLxS0nmSbpb0oKSPSnpCzrZvlnShpI+Z2YnF4+PLKeboVannF0j6vJmdlPQxSb8tqZU5IgBgasY4qsLcvZc3BgBgzr7mqv3+6zc8udExnm1/faO7X9VSkYKMPeMAAACWaHy9MgAAmACXRjkck4wDAAAIRsYBAIBeMBwTAABMXKVQx+ygS5cqijfWJK0qmtvHFsvi1ykric3OHCzxOv0z882rlDShaNCIZ/z01HqXtJu3885ipSdeby+W3Sv347VKTT2ndy6q511Jtx1z90tqlhwAejHWu2NWzJFcKuldkvZLevzi535Fd6zeL+mgzjrzrC0e+xLvtJZ6pLctKl3VjE56nsOs19sZr5PLtyWdzthWLum4pE1JpxY/NyU9sPj54xULm0Q97ymr5y1JL7u9YokBYBBmEDisSzqy2O2Q9k5o64ufqZNZfCLLOontS5Ug/bOohGWljk88a6llWa+3U69PJ5bH6/bp7BObpOh33Z94o/XFIz5Y/LwO6jm8nrdKCgkAaFPFwGFV0Zf4mvZOZOuJZYmjFj2U8VOp9WUlzFuePnEVydq2aN943XZywf7Uwq3F8yZRJPV8Zr8zC/Lqeb8AYIxm0lSxoihNHp/Ekicz2ztinye0pkJOaNLi/GU6+6QWb7ClZv1OqeczSuu5zn23AAB11QgcNnT2lXDGySwvdT72E1oylS5lnNTindfVPHCgnsPqGQDGyWWjnACqZsZBOqetXco/kSXb2ds4oZVdrTa5CM16/9OpZfGJ7US8wLR3co+1kXGQqGeV1DMZBwBYpoqBw6qiK+HE3ukTV9YVb15vfxX8zCpd1avfot7+2zk/4xNi3vrkaIC11Ott0179NO3jQD2H1TMAjNcYJ4CqXuKiE1PII2tf5bxuVtJzr4qTr4vWpV/HV77pE+RaatusE2Fd1PO5ZeqingGgJ/PoHLmic9PhWVe86RNXXgo99Cq4SUBWllLPWp8+8Z3OWFd0hSxFUw7URT2H17OUaMoAAHSt/cAhvS4vZR5vq4x1IVfBeSUvuwItOsHlnbySV8LpuQvyTmjp9voqqOfwepYIHACM0jwyDqZz29HjnyHp8pCr4LIr4LISp080WcviE1X6p7R38iq6Qo4fyRNdcvtt1Z+6WaKeq9QzAGCp6mUcQk5oWcuz9kkvyytZ1TR63okt5KSWXlbYUU/nnsi21XxQBfUcVs8AMGLTH465Julwxp7ptHjeSU0Zy5XzOqt0VU9q6RNL1uvtnNd5qfGs5oj0MSTpaMWyJlHP4fUsSbdWLC8AoLZqp4g4hZ7es+zklXW/hLz9s0pV9USW3i+vvTxelr4CTl8hn069TqbQ08eLn7fRVJEsc/I59Uy2AcDoRX0c6n7x9qdeU0V6z7K0eOiVcN5x65U2/0SWlT4vWp9uj0+e1JL7JZ+30VSRLHvyOfVM4ABg9ObRObLJCa1sWXp9WSnzSl7WWS9+nXUyy7sSLkqtp9+z78ChbFl6vTKWly2ThlPPAIClqhY4JFPo6b3zTlxF64pS53WvgtPbbaeWZV35lnXay1qWPvmlj91WU0X83snn1HP1rAgADND0Mw5rkh5XcIQ6V8Lp5VmlqnslnLcs66o2uS7kqjerw19I+UJQz+H1DABYquqnt/TJKHmUrq6Eq5Qya9v0iSfrSjj5s8qVcPqY6feri3peTj0DQE/mcXfMkLb3vJ8h2+Qdt3pJ9+SluLNOTumTVPwz78SXdyW9pu77OOT9nFs9AwCWqn7gkN676VVwennRe4QqahfPajdPLy/ryLeWcYz4eVuBg0Q9F9UzAIzUPIZjxnt0nUJv62o4r208LwVeJ4We3ib9XnVRz8upZwDo0fQ7R65KOpCxZ9mJrOnVcb3SFp/Q4p9VOuiFptAlNfpfoJ7PXV70PgCApal2itgn6ektlyDrpNCnrCv9OvaVb1K4L/UMAJM2vwmgUKzNPg4AAAxE9cDhQOlWkJoHDtQzAEwaGQecjYwDAGCCamQcvJuSTE3jjAP1DABTN/kJoGxtW+cffrCrskzKo2v1eyFSz9Wc7rsAAFDDLOZxWFl1bRzY7Kosk7K1Wj9jQD1XQ+AAAMtTLeOgXZ23+mhXZZkU026jfalnAJi2WXSOXNGuNsSVcIiVBoED9QwAGKoagcOprsoyKc0DB+oZAKZu8hmHNe3oYt3fVVkm5Tbt1N6XegYAtMHMXiTpFxTdzOB97v6zGdv8j5KuluSS/srdv6fomGQcOkLGAQBQxGWdDsc0s1VJ75b0bZLulHS9mV3n7jcntnmapP9V0nPd/UEzu7TsuAQOHSFwAAAUWcJwzG+UdKu7f0mSzOwjkl4i6ebENj8k6d3u/qAkufu9ZQetHDjsp9NekKaBA/UMAAhw2MxuSLy+xt2vWTy/TNIdiXV3SnpOav+vlCQz+zNFzRlXu/sfFL0hGYeOkHEAAJRpoXPkMXe/KmedZSxLTzK0Julpkr5Z0uWS/tTMnuHuD+W9YaXAYVU7OqjjVXaZrdUGnSOpZwBAC+6U9KTE68sl3Z2xzV+6+5akvzOzWxQFEtfnHbTiqIptevsHWlP9KaepZwCYviVMAHW9pKeZ2ZMl3SXp5ZLSIyZ+R9IrJH3AzA4rarr4UtFBKzdVcCUcpmlTBfUMAGjC3bfN7PWSPqGo/8L73f0mM3urpBvc/brFuhea2c2SdiT9W3cvvHKlj0NH6OMAACjT9QRQ7v4xSR9LLfupxHOX9G8WjyAVA4cd7eeEFmSlQR8H6hkApq/reRy6UrFzJCn0UKsNMg7UMwBgqCp2jtzSEZXODQFFddVkX+oZAKZtCRNAdYKMQ0fIOAAApqjGzJG0vYdoPnMk9QwAUzf5u2OuMDFRsKadI6lnAMAQVWuq2HEdfPh0V2WZlNX6cQP1DAAzsIQJoDpRKXCwXWn9ZFdFmRar31JBPQPADMxiOKYe09n32UK+xxruSz0DAAaoWuCwK4kr4TANMg7UMwDMw+SHY2pH0sPdFGRyGvRxoJ4BAENFxqErZBwAAAVm0TlS2+JKOFT9u2pTzwCAwaoWOGxJurubgkxO/RmnqWcAmIF5ZBxoew9HHwcAQInpBw60vYejjwMAYILIOHSFjAMAoMA8JoDiSjgcGQcAwARVnzmSTnthms4cST0DwKRFnSOnPgEUV8LhyDgAACaIwKErBA4AgBKTH1Wxuyud4oQWZLdB4EA9A8D0zWIeh51d6TgntCCNBlVQzwCAgaqWcZD0SEcFmZqmLRXUMwBM2yyGYz4m6a6OCjI1TQdVUM8AgCGqnHHY7KggU9M040A9A8D0TX44Jin0cDRVAACmiIxDR8g4AACKzGJUBSe0cAQOAIAiswgctiU90FFBpma74b7UMwBgiCoFDi5pq6OCTI033Jd6BoDpG+NwzJW+CwAAAMajUsbhqHTsp6XbuyrMxFxRd0fqubLadQ0AfZnF3THd/ZKuCoI91DMAYKjGF+oAADABsxhVAQAA2jPGwIHOkQAAIBgZBwAAejDWpgoyDgAAIBgZBwAAeuBiAigAADBxZBwAAOjFDCaAAgAA7aBzJAAAmDwyDgAA9ISMAwAAmDQyDgAA9MBloxyOSeAAAEAPxnpbbZoqAABAsPGFOgAATASdIwEAwKSRcQAAoAdMAAUAACaPjAMAAD1wmXZ2x5dxIHAAAKAPLm1vjy9woKkCAAAEI+MAAEAP3E072+M7DQ8+42BmbzKz9y2eX2lmbmbjq+mBo54BACF6PzGY2YnEyw1Jj0raWbx+nbu/o6X3eYakd0r6BkkXu7u1cdyxWFY9p97zU5L+iaR1d99u+/gAMGZRxmF8fRx6Dxzc/UD83Mxuk/Rad/+jDt5qS9JvSvolSb/TwfEHbYn1HL/H92oA/18AgHaNoaniajP7UM66C83sWjM7amZ3mdnbzSwzfHP3W9z9Wkk3dVrgkWqrnuPtJb1F0r/rqrwAMHou7WyvNnr0YexXhB+UdI+kp0q6QNLvSbpD0nv7LNQEVa3nd0h6j6S/X0rpAGCE3E3bW+Nrqhh8xiGPmR2R9GJJb3D3k+5+r6R3SXp5vyWblqr1bGZXSXqupP+0vFICAJZlzBmHKyStSzpqdqaf44qiK2G0J7iezWxFUR+SH3P37cT2AIBzmHZ3xncaHl+J99yhaGTAYXrsd6pKPR+SdJWk31gEDXEO7k4z+253/9PuigkAWIbRNlW4+1FJn5T0TjM7ZGYrZvYUM3t+1vYW2SfpvMXrfWZ2/hKLPEoV6/lhSU+U9PWLx7cvln+DpP+6lAIDwFi4pO3VZo8ejDZwWHilokDgZkkPSvqopCfkbHuFpE3tjarYlHRL1wWciKB69sjfxw9J9y1W3ePujy2ttAAASZKZvcjMbjGzW83sjQXbvWwx8d9Vpcd093ZLCQAAStkzr3L97vXNDvKUlRvdPfNkvxg2/7eSvk3SnZKul/QKd785td1BSb+v6ALx9e5+Q9Fbjj3jAADAOLmkbWv2KPaNkm519y8tsr4fkfSSjO3eJunnJJ0OKTaBAwAA03SZzh4Bd+di2Rlm9ixJT3L33ws96JhHVQAAMG7NxwQeNrNk08I17n7N4nlWSuJM/4TFEPp3SXp1lTckcAAAYLyO5fVxUJRheFLi9eWS7k68PijpGZI+vRhC/xWSrjOz7yrq50DgAABAH1xtZByKXC/paWb2ZEl3KZrx93vOvL37w5IOx6/N7NOSfqKsc2SlwGHDzB9XZYcZe0jSqZq37qaeqzkaRdyX9F0OABiSxQy+r5f0CUUT8r3f3W8ys7dKusHdr6tz3EqBw2FJP1HnXWboPzbYl3qu5sek2/suAwBU1n3GQe7+MUkfSy37qZxtvznkmJUCh3WlumMi13rDfalnAJg4l7TVdyGqqxQ4XLRPeumVHZVkYv79bfX3pZ4r+pu+CwAA81Gtc+QBSc/rpiCTc6zBvtRzNQQOAMbIJe30XYjqqgUOByV9SzcFmZz/0mBf6rma9/VdAACYj2qBwyFJL+imIJPzHxrsSz0DwDx03DmyC5UCh1Nr+/TZS5/cVVkm5dTa3zXYl3qu5q/7LgAAzEalwOFBXaSP6mVdlWVSHmyQP6eeq3pb3wUAgOqWMByzC5UChy2t6249oauyTMpWgwGZ1DMAzMBcAod7daSrskxK08CBegYADFGlwGFXKzqug12VZVJ2G9yxnHoGgBmYQ8ZhV6bHdF5XZZmU3cy7mYbvSz0DAIaIu2MCANCHOWQc1rSji3V/V2WZlLUG04FRzwCAoap4k6stXap7uirLpKw3uHMJ9QwAMzH1jMO6tvREHe2qLJPSNHCgngFg4uZwd8x9Oq2v0i1dlWVS9ul0o32pZwDAEFUKHPZrU8/UF7oqy6Ts12ajfalnAJi4Odwdc+OB03r2h7kvQIiNB5rsSz0DAIap2nDM45I+1U1BJud4w32pZwCYtjkMx9QJSZ/ppiCTc6LhvtQzAGCAKgUOj56Wvvg3XRVlWh5tsi/1DADTN4eMwylJn++oIFNzquG+1DMATNwcAodNib7+geqPqaCeAQDDxb0qAADoywgzDvXv/QwAAGanUsbBJK13VJCpqX9TbeoZAGZhDn0cViTt76ggU9MklUM9AwCGisChIwQOAIBCc8g4rEl6fEcFmZomvU6pZwCYgTncHXNF0qGOCjI1TTMO1DMAYIhoqugITRUAgEJzuDvmqqSDHRVkalYb7ks9AwCGqHLgQAo9TNPAgXoGgBmYeufI9RXpMnLoQdYbzDlNPVd0su8CAMB8VOvjsCJtXNBVUaZlpcHtManniggcAIzRHIZjakUSJ7QwDzXYl3oGgOkjcMBZmg6roJ4BAANULXBYFSe0UE17R1LPADBtc5gASquSLuymIJPTNHCgngEAA1QtcFiX9MRuCjI5TW5vST0DwPTNYQIo2t4roI8DAGCCaKroCk0VAIAyjKrAGWQcAABFZjEckyvhcGQcAAATVC1wOE902gt1XsN9qWcAmDaGY+IsZBwAABNEH4eu0McBAFBkFsMxuRIOR8YBADBB1QOHQ90UZHKaBg7UMwBM2xxGVfiKtEUKPYg3aKqgngFgJqYeOGytrunuCy/qqiyTsrX6YIN9qedq7uu7AAAwG5UCh12taFMbXZVlUnb1cIN9qWcAmLw5DMfc0YqO62BXZZmUHd3TYF/qGQAwTJUzDqe4Eg6y22A8JvUMADMwh+GYu1rVcR3oqiyTsttgWAX1DAAYqmqdI7Wue3Wkq7JMypbWG+1LPQPAxM1hOCYp9HA0VQAACs0lcKDTXpimgQP1DAAYohoZh/1dlWVSmmccqGcAmLQ5DMdkfoFwTQMH6hkAMESVO0feQ6e9IE07R1LPADAD0x+OuaJNUuhBmmccqGcAwPAwqqIjjKoAABSay6gKTmhhCBwAAIXmETgYKfRAu7JG+1LPAIAhIuPQETIOAIBCcxiOua01HdPFXZVlUrarVe05+1LPAICmzOxFkn5B0qqk97n7z6bW/xtJr1XUaHKfpB9099uLjlnp7OZa0WM751cq9Fx5g4wD9QwAM9Dx3THNbFXSuyV9m6Q7JV1vZte5+82JzT4n6Sp3P2VmPyrp5yT9i6LjVmuq2DGdOkHbe4jdnQZ9HKhnAEBz3yjpVnf/kiSZ2UckvUTSmcDB3f84sf1fSvq+soNWyzjsrur0Cdreg+zWv6029QwAM9HtqIrLJN2ReH2npOcUbP8aSR8vO2i1hvhdk06TQg+yWz/jQD0DwAy0MxzzsJndkHh9jbtfs3iedSLyrIOY2fdJukrS88vesFrgsC3pWIMT4pw0+WegngEAYY65+1U56+6U9KTE68sl3Z3eyMy+VdL/Jun57v5o2RtWzDhIOlFpj/nabbgv9QwA09b9cMzrJT3NzJ4s6S5JL5f0PckNzOxZkt4r6UXufm/IQasHDqcr7TFfTQMH6hkA0IC7b5vZ6yV9QtFwzPe7+01m9lZJN7j7dZJ+XtIBSb9lZpL0ZXf/rqLjmntmc0f2xmb3SSoc34kzrnD3S+rsSD1XVruuAaAvdv5VrstuKN+wyN/ZjQVNFZ2oNqqCL+eloJ4BAENVf3pDAABQ3xxucgUAAFo0wsCh/rzIAABgdsg4AADQh5HeHZOM/SNiVAAADWxJREFUAwAACEbGAQCAPnR8d8yukHEAAADByDgAANAHhmMCAIBgIw0caKoAAADByDgAANAHhmMCAICpI+MAAEBfGI4JAACmjIwDAAB98b4LUB0ZBwAAEIzAAQAABCNwAAAAwQgcAABAMAIHAAAQjMABAAAEYzgmAAC9GOec0wQOAAD0Ypy3xxx8U4WZvcnM3rd4fqWZuZkR8LSMegYAhOj9xGBmJxIvNyQ9qr3Zu1/n7u9o8b1+XNL/Imm/pP9L0o+6+6NtHX/IllXPZvZyST8t6SsW7/FxSf/K3R9p4/gAMB3jbKroPePg7gfih6QvS/rOxLJfb+t9zOyfSnqjpBdIulLSP1R0gpuFZdWzpD+T9Fx3v1BRHa9JenuLxwcA9Kj3wKGMmV1tZh/KWXehmV1rZkfN7C4ze7uZreYc6lWSrnX3m9z9QUlvk/Tqjoo9Om3Vs7vf4e7HEot2JD21izIDwLjFfRyaPJav96aKhj4o6R5FJ6YLJP2epDskvTdj26+V9LuJ138l6YiZXezu93dd0JGrUs8ys+dJ+n1JhySdkvTPl1NMABgTmiqWysyOSHqxpDe4+0l3v1fSuyS9PGeXA5IeTryOnx/srpTjV6Oe5e6fWTRVXC7p5yXdtoyyAgC6N+aMwxWS1iUdNbN42YqiK+EsJxRdAcfi58c7Kd10VK3nM9z9LjP7A0kfkfTszkoIAKM0zozDmAOHOxT12j/s7iENPTdJ+jpJv7l4/XWS7qGZolTVek5bk/SUdosEAOjLaJsq3P2opE9KeqeZHTKzFTN7ipk9P2eXX5X0GjP7GjO7SNJPSvrAkoo7WlXr2cy+18z+gUWukPQzkv7LMssMAOMxvs6Row0cFl4p6TxJN0t6UNJHJT0ha0N3/wNJPyfpjyXdvni8ZTnFHL3gepb0NZL+XFHT0J9JukXSDy2hjACAJTB377sMAADMjtkzXfrthkf5yhvd/apWChRozH0cAAAYMe5VAQAAJo6MAwAAvRjncEwyDgAAIBgZBwAAejHOPg6VAgc7dNh1yZUdFWVi7rtN/sgxK9/wXNRzRV+68Zi7X9J3MQBgDqplHC6+UvqpG7opydRc3WB0DPVczavt9r6LAADVjbOPQ7XAYUfSQ90UZHJ2Gu5LPQPAxM2gqUK7kk53U5DJ2W24L/UMABggAoeuEDgAAArNoanCxQktVJOZvKlnAMBAkXHoChkHAEChOfRxoNNeODpHAgAmqHpTxfiCo340baqgngFg4ujjgCT6OAAASo3vKpE+Dl2hjwMAYIJoqugKTRUAgEJzaKrgSjgcGQcAwARVDxxOdFOQyWkaOFDPADBxc8g4kEIPR1MFAGCCCBy6QuAAACg0zi/7aoGDNMbfcZyoZwCYuDk0VdBpLxydIwEAE0Tg0BUCBwBAIZoq0CbqGQAwQHSO7AqdIwEAhebQx4ETWjgCBwDABBE4dIXAAQBQaJxf9twdc4ioZwCYgTk0VaxLOlJ77z1lAVbe+vTyrO22Kz4Pkk4fpA+Q/sNvS9qp+iYJO5IeKTh+qPWc5ek/XHq75Hqr/rZrFZ/nLcv7/0ovvzOwXACAxpo1VSSflx2pysm6i8ChahnOSAYNIQFDm5pGovH+6cAgLudaznbJ9fHvXyGASO4+viwcACzJHJoqpPCTel1FxykLFEJeB2saMDTt5JAXNJT9Qll/0uSxkkFESAARr2sQQMRvUVT0KkFGslgAgKUaXufItgOHyoqaJUIChrbbq6r+Qunt03/irCxEUQCRXlcjgMgqFgDM3hz6OHQdOLTV96GWphmGNv/4bVZ0XntSaACRbr6I19cMIPKKRWABAKMwvCmnOz+BVO3oWHebJk0Vee/RRDJ7UEVWu0AyoIilt6nZpBGyPOvtAGB05t7HYdDKggWpPKsQEiwkt2vaxyHvuKHSJ/ayfg7SuU0V8bZF25RlJKTaWYkio/w/BICko5+Qrj7c8CDHWilKBRO8bss66eadZeo0QSyjX0Mbx0zvvx6wrk6AkBWQlPWzkDoJJgBgRNz9RX2XoY6RBw55V/WhgULWtqGZhbxtu8o4VL3EzusUGcsKJEI6TCb3LwoiksfLCiSyfkeCCQAYuhEEDmUn4KITapUhjU0yC11kHJrm4rP2z+ocKe2d4MuCg+QxyjIReccrKlvR35qgAgCGoGLgkDd0JPQwTU+GISfoKtmBvO2LMgkh7yV1N49DE1mdGqVzswtF26eXZe1bpT9F1j5Z+wIAhqBm4JA3E2FVdU+OZe9XdNw6zQ51929D02CrSpNFSEfJvGVlmYn0e4YMmRjf+GYAmLqaTRVDOUmWlaNOM0YbwcK2hnN7zDpNFun9QoOI5PKi982rt6J9844FAFimGhmHZY6DqxKg1AkS8ta10dzRVBfzOMRCswNF2YS8bYuaHKoEMVmKfgcAwDK01MdhGUJPFGXlq3ryr7v9mEZVlM0uGbJ9leGfyfVV7r1B0wUA9K2ljMOyOkcmhZxE2urrUKdfQ99NFXn7V+17UCcbUXR8FaxPb5N1fABAn1rKOHR9JdhWk0XZsdockdHlqIqyoKBI3siKqtvn3aIy71be8T6xvLIyugIAhmxETRVJbTVbVJmrYZmjKepmHELT/lVGOmT1Z8jbvqxZIbSPQshQTQBAHwbWNb3LYZ1tTRQVun3Tm1yVvVeokE6R6fep2/+hbufHus0TA/v3BYAZqPHN20ebc9WTZ9vNFXUCi6FMAFWlY2LRsMv09kUBQVlmoWpGIa+pgv4PALBsI22qkKqdNOoMo2w602Qb2joxho6waGPa6KJ9YlX7aAzlfw4AMPB5HPKEnkjqZh7ayDrslrx3kV1Jmw32T8vLIMSyOjrmdXLM6xRZJG9K67SijpUAgCEYWCNxkyvLJs0TRfv3Pe10m30c0ser0gxRp8miaL+s/YuOk4UAAwCWbWRNFW00T5Qdq41Aoes+DlVS/UUn8y6aLKpMPJW1f9Gx0mjCAIBlqxE4tK2Npo9ljqqoco+LJtoejhkygqJspETVURjpfdL7Ze2fNIRmMQBA0khGVcSWMRFU02Chq/qpGpgUXdlXCSKaTE2d3ie9X9b+STRFAMDQDKSPQ9Or9ZCTdVszRi4jWGijSaitYZhtDs3Meu/0/kk0RQDA0Aysj0OTE3DdPg1F+4YGCVnbNRlVETp6peoohDojIrqWN2NliKH9LgAwfQzHDFrWRkfKuprezCvvij905ETdZo30cdLHyjpm1rGLDOF/EQDmZcCXbHUzG3UyC3nrQrMLWdsNdVRF6MiJ0M6RRXe8DO1nkVYlEAIALNPAmiqqanqzqybBQtVt66pyVV1leufQ7EJZv4aijpGhmYj0MYsM6f8PAOZnwBmHpKop6bKTS9NAIL1d2yezsiahKqMQ6tykqiyAiLcNnW2yLBORPGaWkfybAsAMjLSPg9S87V9q1hSR3i69TR83uaqzT/q22Xm3y846+YeMzkjvU7Q+uU3SUP7nAAAjuZSrckLsesbIsoAh3qbNybLaGnUQ0kzRdlNG1vun98vbpmxbAMCyDShwaJLub3soZpuZiCaaXmnn3VxqS+VND00CiOS2We+f3C9r36Jt0wgqAGCZRtw5ss9ppkO2adpU0VYfByn7JF4lMGjSvyHvuFn75u1fZCj/jwAwDwPKOBQZylTTdbdpKvT3D+mEWCezEBpk5G2b3idrv/T+SWQVAGAoWs44VGm3bkMXU0232dehrrbmcagzh0NIAFF2rKL3Th87iamnAWDoWs44LOMLvuncDUXHqDv0Mmubtu8kGvJ7h/YrSAcHIRmI0H4SVd47LXSCKABAX0bw7dzmHA5NO0nWabpoqm4mo6xfQdm26cAgNHgoe+/Q+2uE3FUTALBsA/s27uMmV22OlhhK0JA+RpV+COnt6o6qyHvvqmVIv3fSwP59AWAGanzzFp3M8g7X1QQ+TW921eZQzDaDhpCJtkJnY5TyMwAhWYW841XJPoTel6JqJ0gmhgKAZWv5km1ZX+RNA4Yqx6gbNLTdxyHvfbLWFQ3LrBo8VMkM1Gm6SO8f+l4AgD4MONdb9wq+q7kbsrYLzVg00XTq6TpzMIQ0SVS5B0byvbP2SWPCJwAYqoEEDk3T/E2GZYae6KeUFm/SJFFlu7wMRLxPrMq/IUMzAaBPI545Umo+NDO070NfQUMbdV108m7zeHWCh+S+ytkfADAkK30XoJ5ttTOfQ5Ptu26maDNAa/s+G13VaZW/KwCgDyO6xGtzPoc6xyszxhNeSFNEaHNF2bZVOj4yERQADNXAvpXbOPm2OZ9D3vZdN9d0NSKj7siIKnNBhN7Qqsn8DUXHBwB0qUYfh7bao/sYfVD1PZvcy2IsqkwnXeVvHjp/Q93+F2OucwAYrwZn/6F8cbcRMAypw+fYhIyc6DKAAAAsk7mHp8XN7D5Jt3dXnEm5wt0vqbMj9VxZ7boGAFRTKXAAAADzNtLhmAAAoA8EDgAAIBiBAwAACEbgAAAAghE4AACAYAQOAAAgGIEDAAAIRuAAAACCETgAAIBgBA4AACAYgQMAAAhG4AAAAIIROAAAgGAEDgAAIBiBAwAACEbgAAAAgv3/sR+Ot7ader0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x745.2 with 26 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ecco.plot_tiles(grid.rA, show_colorbar=True);\n",
    "'Model grid cell surface area [m^2]'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 2: Loading the model grid parameters using ``load_ecco_grid_nc``\n",
    "\n",
    "A more advanced routine, ``load_ecco_grid_nc``, allows you to load only a subset of tiles and vertical levels.  If no optional parameters are given, the entire grid object is loaded, just like ``open_dataset``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (i: 90, i_g: 90, j: 90, j_g: 90, k: 50, k_l: 50, k_p1: 51, k_u: 50, tile: 13)\n",
       "Coordinates:\n",
       "  * i          (i) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * i_g        (i_g) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j_g        (j_g) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * k          (k) int64 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
       "  * k_u        (k_u) int64 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
       "  * k_l        (k_l) int64 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
       "  * k_p1       (k_p1) int64 0 1 2 3 4 5 6 7 8 9 ... 42 43 44 45 46 47 48 49 50\n",
       "  * tile       (tile) int64 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    XC         (tile, j, i) float32 -111.60647 -111.303 ... -111.86579\n",
       "    YC         (tile, j, i) float32 -88.24259 -88.382515 ... -88.07871 -88.10267\n",
       "    XG         (tile, j_g, i_g) float32 -115.0 -115.0 ... -102.928925 -108.95171\n",
       "    YG         (tile, j_g, i_g) float32 -88.17569 -88.31587 ... -88.02409\n",
       "    CS         (tile, j, i) float32 0.06157813 0.06675376 ... -0.9983638\n",
       "    SN         (tile, j, i) float32 -0.99810225 -0.9977695 ... -0.057182025\n",
       "    Z          (k) float32 -5.0 -15.0 -25.0 -35.0 ... -5039.25 -5461.25 -5906.25\n",
       "    Zp1        (k_p1) float32 0.0 -10.0 -20.0 -30.0 ... -5244.5 -5678.0 -6134.5\n",
       "    Zu         (k_u) float32 -10.0 -20.0 -30.0 -40.0 ... -5244.5 -5678.0 -6134.5\n",
       "    Zl         (k_l) float32 0.0 -10.0 -20.0 -30.0 ... -4834.0 -5244.5 -5678.0\n",
       "    rA         (tile, j, i) float32 362256450.0 363300960.0 ... 361119100.0\n",
       "    dxG        (tile, j_g, i) float32 15584.907 15589.316 ... 23142.107\n",
       "    dyG        (tile, j, i_g) float32 23210.262 23273.26 ... 15595.26 15583.685\n",
       "    Depth      (tile, j, i) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
       "    rAz        (tile, j_g, i_g) float32 179944260.0 180486990.0 ... 364150620.0\n",
       "    dxC        (tile, j, i_g) float32 15583.418 15588.104 ... 23406.256\n",
       "    dyC        (tile, j_g, i) float32 11563.718 11593.785 ... 15578.138\n",
       "    rAw        (tile, j, i_g) float32 361699460.0 362790240.0 ... 364760350.0\n",
       "    rAs        (tile, j_g, i) float32 179944260.0 180486990.0 ... 364150620.0\n",
       "    drC        (k_p1) float32 5.0 10.0 10.0 10.0 ... 399.0 422.0 445.0 228.25\n",
       "    drF        (k) float32 10.0 10.0 10.0 10.0 10.0 ... 387.5 410.5 433.5 456.5\n",
       "    PHrefC     (k) float32 49.05 147.15 245.25 ... 49435.043 53574.863 57940.312\n",
       "    PHrefF     (k_p1) float32 0.0 98.1 196.2 ... 51448.547 55701.18 60179.445\n",
       "    hFacC      (k, tile, j, i) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    hFacW      (k, tile, j, i_g) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    hFacS      (k, tile, j_g, i) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    maskC      (k, tile, j, i) bool False False False ... False False False\n",
       "    maskW      (k, tile, j, i_g) bool False False False ... False False False\n",
       "    maskS      (k, tile, j_g, i) bool False False False ... False False False\n",
       "    maskCtrlW  (k, tile, j, i_g) bool False False False ... False False False\n",
       "    maskCtrlS  (k, tile, j_g, i) bool False False False ... False False False\n",
       "    maskCtrlC  (k, tile, j, i) bool False False False ... False False False\n",
       "Data variables:\n",
       "    *empty*\n",
       "Attributes:\n",
       "    Conventions:              CF-1.6\n",
       "    geospatial_lat_max:       90.0\n",
       "    geospatial_lat_min:       -90.0\n",
       "    geospatial_lon_max:       180.0\n",
       "    geospatial_lon_min:       -179.99919\n",
       "    geospatial_vertical_max:  -5.0\n",
       "    geospatial_vertical_min:  -5906.25\n",
       "    history:                  Created by calling `open_mdsdataset(extra_metad...\n",
       "    nx:                       90\n",
       "    ny:                       90\n",
       "    nz:                       50\n",
       "    source:                   MITgcm\n",
       "    title:                    netCDF wrapper of MITgcm MDS binary data"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_dir = ECCO_dir + 'nctiles_grid'\n",
    "\n",
    "grid = ecco.load_ecco_grid_nc(grid_dir, 'ECCOv4r3_grid.nc')\n",
    "grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively we can load just a subset of tiles and vertical levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (i: 90, i_g: 90, j: 90, j_g: 90, k: 4, k_l: 4, k_p1: 4, k_u: 4, tile: 3)\n",
       "Coordinates:\n",
       "  * i          (i) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * i_g        (i_g) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j_g        (j_g) int64 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * k          (k) int64 0 1 2 3\n",
       "  * k_u        (k_u) int64 0 1 2 3\n",
       "  * k_l        (k_l) int64 0 1 2 3\n",
       "  * k_p1       (k_p1) int64 0 1 2 3\n",
       "  * tile       (tile) int64 1 10 12\n",
       "    XC         (tile, j, i) float32 -37.5 -36.5 -35.5 ... -105.58465 -111.86579\n",
       "    YC         (tile, j, i) float32 -56.73891 -56.73891 ... -88.07871 -88.10267\n",
       "    XG         (tile, j_g, i_g) float32 -38.0 -37.0 ... -102.928925 -108.95171\n",
       "    YG         (tile, j_g, i_g) float32 -57.005695 -57.005695 ... -88.02409\n",
       "    CS         (tile, j, i) float32 1.0 1.0 1.0 ... -0.98536175 -0.9983638\n",
       "    SN         (tile, j, i) float32 -0.0 6.52406e-15 ... -0.1704766 -0.057182025\n",
       "    Z          (k) float32 -5.0 -15.0 -25.0 -35.0\n",
       "    Zp1        (k_p1) float32 0.0 -10.0 -20.0 -30.0\n",
       "    Zu         (k_u) float32 -10.0 -20.0 -30.0 -40.0\n",
       "    Zl         (k_l) float32 0.0 -10.0 -20.0 -30.0\n",
       "    rA         (tile, j, i) float32 3624512000.0 3624512000.0 ... 361119100.0\n",
       "    dxG        (tile, j_g, i) float32 60542.324 60542.324 ... 23142.107\n",
       "    dyG        (tile, j, i_g) float32 59441.125 59441.125 ... 15595.26 15583.685\n",
       "    Depth      (tile, j, i) float32 3284.1084 3485.7 3485.7 ... 0.0 0.0 0.0\n",
       "    rAz        (tile, j_g, i_g) float32 3584245000.0 ... 364150620.0\n",
       "    dxC        (tile, j, i_g) float32 60975.85 60975.85 ... 23865.428 23406.256\n",
       "    dyC        (tile, j_g, i) float32 59201.66 59201.66 ... 15585.765 15578.138\n",
       "    rAw        (tile, j, i_g) float32 3624512000.0 3624512000.0 ... 364760350.0\n",
       "    rAs        (tile, j_g, i) float32 3584245000.0 3584245000.0 ... 364150620.0\n",
       "    drC        (k_p1) float32 5.0 10.0 10.0 10.0\n",
       "    drF        (k) float32 10.0 10.0 10.0 10.0\n",
       "    PHrefC     (k) float32 49.05 147.15 245.25 343.35\n",
       "    PHrefF     (k_p1) float32 0.0 98.1 196.2 294.3\n",
       "    hFacC      (k, tile, j, i) float32 1.0 1.0 1.0 1.0 1.0 ... 0.0 0.0 0.0 0.0\n",
       "    hFacW      (k, tile, j, i_g) float32 1.0 1.0 1.0 1.0 1.0 ... 0.0 0.0 0.0 0.0\n",
       "    hFacS      (k, tile, j_g, i) float32 1.0 1.0 1.0 1.0 1.0 ... 0.0 0.0 0.0 0.0\n",
       "    maskC      (k, tile, j, i) bool True True True True ... False False False\n",
       "    maskW      (k, tile, j, i_g) bool True True True True ... False False False\n",
       "    maskS      (k, tile, j_g, i) bool True True True True ... False False False\n",
       "    maskCtrlW  (k, tile, j, i_g) bool True True True True ... False False False\n",
       "    maskCtrlS  (k, tile, j_g, i) bool True True True True ... False False False\n",
       "    maskCtrlC  (k, tile, j, i) bool True True True True ... False False False\n",
       "Data variables:\n",
       "    *empty*\n",
       "Attributes:\n",
       "    Conventions:              CF-1.6\n",
       "    geospatial_lat_max:       72.043434\n",
       "    geospatial_lat_min:       -88.02409\n",
       "    geospatial_lon_max:       51.0\n",
       "    geospatial_lon_min:       -128.0\n",
       "    geospatial_vertical_max:  -5.0\n",
       "    geospatial_vertical_min:  -35.0\n",
       "    history:                  Created by calling `open_mdsdataset(extra_metad...\n",
       "    nx:                       90\n",
       "    ny:                       90\n",
       "    nz:                       4\n",
       "    source:                   MITgcm\n",
       "    title:                    netCDF wrapper of MITgcm MDS binary data"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_subset = ecco.load_ecco_grid_nc(grid_dir, 'ECCOv4r3_grid.nc', tiles_to_load = [1, 10, 12], k_subset=[0,1,2,3])\n",
    "grid_subset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "notice that ``grid_subset`` only has 3 tiles (9,10, 11) and 4 depth levels (0, 1, 2, 3), as expected.\n",
    "\n",
    "Let's plot ``hFacC`` and ``rA`` again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Model grid cell surface area [m^2] in tiles 1, 10, and 12 '"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x745.2 with 26 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ecco.plot_tiles(grid_subset.hFacC.sel(k=0), show_colorbar=True, cmap='gray');\n",
    "'Model grid cell surface area [m^2] in tiles 1, 10, and 12 '"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that 10 of the 13 tiles are blank because they were not loaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Model grid cell surface area [m^2]'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x745.2 with 26 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ecco.plot_tiles(grid_subset.rA, show_colorbar=True);\n",
    "'Model grid cell surface area [m^2]'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "Now you know two ways to load the ECCOv4 grid parameter file!  "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
